Abstract
Following a national lockdown in response to the Covid-19 pandemic, state governments in Germany published lists of “essential” occupations that were considered necessary to maintain basic services such as health care, social care, food production and transport. This paper examines working conditions in these essential occupations and identifies clusters of similar jobs. Differences across clusters are highlighted using detailed data on job characteristics including working conditions, tasks and educational requirements. Two clusters with favourable or average working conditions account for more than three-quarters of jobs in essential occupations. Another two clusters, comprising 20% of jobs in essential occupations, are associated with unfavourable working conditions such as low pay, job insecurity, poor prospects for advancement and low autonomy. These latter clusters exhibit high shares of migrants. An Oaxaca–Blinder decomposition is used to investigate which individual characteristics explain why migrants are more likely to have unfavourable working conditions. The results suggest that lacking proficiency in the host-country language is the main barrier to improving migrants’ working conditions.
Highlights
During the first wave of the Covid-19 pandemic in spring of 2020, authorities in the German states published lists of occupations considered “essential” for the maintenance of basic services with the view to providing children of workers in these occupations with preferential access to emergency childcare (Koebe et al, 2020)
The Latent Class (Cluster) Analysis (LCA) in this paper examines relationships among seven important job characteristics: hourly wage, contract type, flexibility of working hours, unpaid overtime, job insecurity and indicators of bad work relations as well as bad working conditions
Bad working conditions and routine tasks mostly arise in a few occupations, notably cleaning, logistics/warehousing, social work and certain forms of care
Summary
The Covid-19 pandemic has highlighted a number of “essential” or “system-relevant” occupations, which were exempted from the limitations that other occupations faced during the pandemic, notably during lockdowns. A better understanding of essential occupations can support countries’ efforts to weather such crises and help increase their resilience in the longer run While this newly emerged class of essential occupations has hardly been explored, it has been noted that they include many jobs with low pay and low prestige, comparatively often filled by migrants (Gelatt, 2020; Fasani and Mazza, 2020a; Koebe et al, 2020). Through a Latent Class Analysis, this paper delimits clusters of jobs across essential occupations that share important job quality characteristics. This clustering approach reflects that working conditions are multi-dimensional, which requires using a number of indicators – not just the level of pay. Between essential occupations designated by various countries implies that insights obtained here might generalise to other countries
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